For complex classification systems, data is usually gathered from multiple sources of information that have varying degree of reliability. In fact, assuming that the different sources have the same relevance in describing all the data might lead to...

For complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing...

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called "expert" or "learner") to determine the true class of a given pattern....